IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i5p713-d811961.html
   My bibliography  Save this article

Impact of Urban Form on CO 2 Emissions under Different Socioeconomic Factors: Evidence from 132 Small and Medium-Sized Cities in China

Author

Listed:
  • Ran Guo

    (School of Architecture, Harbin Institute of Technology, Harbin 150006, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150006, China)

  • Hong Leng

    (School of Architecture, Harbin Institute of Technology, Harbin 150006, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150006, China)

  • Qing Yuan

    (School of Architecture, Harbin Institute of Technology, Harbin 150006, China
    Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150006, China)

  • Shiyi Song

    (School of Architecture, Xi’an University of Architecture and Technology, Xi’an 710055, China)

Abstract

The accurate estimation of the impact of urban form on CO 2 emissions is essential for the proposal of effective low-carbon spatial planning strategies. However, few studies have focused on the relationship between urban form and CO 2 emissions in small and medium-sized cities, and it is especially unclear whether the relationship varies across cities with different socioeconomic characteristics. This study took 132 small and medium-sized cities in the Yangtze River Delta in China to explore how urban form affects CO 2 emissions, considering the socioeconomic factors of industrial structure, population density, and economic development level. First, nighttime light data (DMSP-OLS and NPP-VIIRS) and provincial energy data were used to calculate CO 2 emissions. Second, four landscape metrics were used to quantify the compactness and complexity of the urban form based on Chinese urban land-use data. Finally, panel data models were established to analyze whether and how different socioeconomic factors impacted the relationship between urban form and CO 2 emissions. The results showed that the three socioeconomic factors mentioned above all had obvious influences on the relationship between urban form and per capita CO 2 emissions in small and medium-sized cities. The effect of compactness on per-capita CO 2 emissions increased with a rise in the proportion of the tertiary industry, population density, and per-capita GDP. However, compactness shows no effects on per-capita CO 2 emissions in industrial cities and low-development-level cities. The effect of complexity on per-capita CO 2 emissions only increased with the rise in population density. The results may support decision-makers in small and medium-sized cities to propose accurate, comprehensive, and differentiated plans for CO 2 emission control and reduction.

Suggested Citation

  • Ran Guo & Hong Leng & Qing Yuan & Shiyi Song, 2022. "Impact of Urban Form on CO 2 Emissions under Different Socioeconomic Factors: Evidence from 132 Small and Medium-Sized Cities in China," Land, MDPI, vol. 11(5), pages 1-20, May.
  • Handle: RePEc:gam:jlands:v:11:y:2022:i:5:p:713-:d:811961
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/5/713/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/11/5/713/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhao, Jincai & Ji, Guangxing & Yue, YanLin & Lai, Zhizhu & Chen, Yulong & Yang, Dongyang & Yang, Xu & Wang, Zheng, 2019. "Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets," Applied Energy, Elsevier, vol. 235(C), pages 612-624.
    2. Tao Lin & Yunjun Yu & Xuemei Bai & Ling Feng & Jin Wang, 2013. "Greenhouse Gas Emissions Accounting of Urban Residential Consumption: A Household Survey Based Approach," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-12, February.
    3. He, Sanwei & Yu, Shan & Li, Guangdong & Zhang, Junfeng, 2020. "Exploring the influence of urban form on land-use efficiency from a spatiotemporal heterogeneity perspective: Evidence from 336 Chinese cities," Land Use Policy, Elsevier, vol. 95(C).
    4. Liu, Xingjian & Wang, Mingshu & Qiang, Wei & Wu, Kang & Wang, Xiaomi, 2020. "Urban form, shrinking cities, and residential carbon emissions: Evidence from Chinese city-regions," Applied Energy, Elsevier, vol. 261(C).
    5. Wang, Shaojian & Liu, Xiaoping, 2017. "China’s city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces," Applied Energy, Elsevier, vol. 200(C), pages 204-214.
    6. Tian, Guangjin & Jiang, Jing & Yang, Zhifeng & Zhang, Yaoqi, 2011. "The urban growth, size distribution and spatio-temporal dynamic pattern of the Yangtze River Delta megalopolitan region, China," Ecological Modelling, Elsevier, vol. 222(3), pages 865-878.
    7. Wang, Shaojian & Zeng, Jingyuan & Huang, Yongyuan & Shi, Chenyi & Zhan, Peiyu, 2018. "The effects of urbanization on CO2 emissions in the Pearl River Delta: A comprehensive assessment and panel data analysis," Applied Energy, Elsevier, vol. 228(C), pages 1693-1706.
    8. Harris, Richard D. F. & Tzavalis, Elias, 1999. "Inference for unit roots in dynamic panels where the time dimension is fixed," Journal of Econometrics, Elsevier, vol. 91(2), pages 201-226, August.
    9. Pedroni, Peter, 2004. "Panel Cointegration: Asymptotic And Finite Sample Properties Of Pooled Time Series Tests With An Application To The Ppp Hypothesis," Econometric Theory, Cambridge University Press, vol. 20(3), pages 597-625, June.
    10. Littlefair, Paul, 1998. "Passive solar urban design : ensuring the penetration of solar energy into the city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 2(3), pages 303-326, September.
    11. Zhou, Yang & Liu, Yansui, 2016. "Does population have a larger impact on carbon dioxide emissions than income? Evidence from a cross-regional panel analysis in China," Applied Energy, Elsevier, vol. 180(C), pages 800-809.
    12. Karen C. Seto & Robert K. Kaufmann, 2003. "Modeling the Drivers of Urban Land Use Change in the Pearl River Delta, China: Integrating Remote Sensing with Socioeconomic Data," Land Economics, University of Wisconsin Press, vol. 79(1), pages 106-121.
    13. Fang, Chuanglin & Wang, Shaojian & Li, Guangdong, 2015. "Changing urban forms and carbon dioxide emissions in China: A case study of 30 provincial capital cities," Applied Energy, Elsevier, vol. 158(C), pages 519-531.
    14. Camagni, Roberto & Gibelli, Maria Cristina & Rigamonti, Paolo, 2002. "Urban mobility and urban form: the social and environmental costs of different patterns of urban expansion," Ecological Economics, Elsevier, vol. 40(2), pages 199-216, February.
    15. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Chen, Zuoqi & Liu, Rui & Li, Linyi & Wu, Jianping, 2016. "Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis," Applied Energy, Elsevier, vol. 168(C), pages 523-533.
    16. Yu Song & Guofan Shao & Xiaodong Song & Yong Liu & Lei Pan & Hong Ye, 2017. "The Relationships between Urban Form and Urban Commuting: An Empirical Study in China," Sustainability, MDPI, vol. 9(7), pages 1-17, July.
    17. Wang, Dongeen & Lin, Tao, 2014. "Residential self-selection, built environment, and travel behavior in the Chinese context," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(3), pages 5-14.
    18. Wang, Shaojian & Liu, Xiaoping & Zhou, Chunshan & Hu, Jincan & Ou, Jinpei, 2017. "Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities," Applied Energy, Elsevier, vol. 185(P1), pages 189-200.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Umar Nawaz Kayani & Mochammad Fahlevi & Roohi Mumtaz & Reema Al Qaruty & Muzaffar Asad, 2023. "The Nexus between Carbon Emissions and Per Capita Income of Households: Evidence from Japanese Prefectures," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 567-572, November.
    2. Yichen Ding & Yaping Huang & Lairong Xie & Shiwei Lu & Leizhou Zhu & Chunguang Hu & Yidan Chen, 2022. "Spatial Patterns Exploration and Impacts Modelling of Carbon Emissions: Evidence from Three Stages of Metropolitan Areas in the YREB, China," Land, MDPI, vol. 11(10), pages 1-18, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Changlong Sun & Yongli Zhang & Wenwen Ma & Rong Wu & Shaojian Wang, 2022. "The Impacts of Urban Form on Carbon Emissions: A Comprehensive Review," Land, MDPI, vol. 11(9), pages 1-20, August.
    2. Hui Wang & Guifen Liu & Kaifang Shi, 2019. "What Are the Driving Forces of Urban CO 2 Emissions in China? A Refined Scale Analysis between National and Urban Agglomeration Levels," IJERPH, MDPI, vol. 16(19), pages 1-19, September.
    3. Yongxing Li & Wei Guo & Peixian Li & Xuesheng Zhao & Jinke Liu, 2023. "Exploring the Spatiotemporal Dynamics of CO 2 Emissions through a Combination of Nighttime Light and MODIS NDVI Data," Sustainability, MDPI, vol. 15(17), pages 1-17, August.
    4. Song, Weize & Zhang, Xiaoling & An, Kangxin & Yang, Tao & Li, Heng & Wang, Can, 2021. "Quantifying the spillover elasticities of urban built environment configurations on the adjacent traffic CO2 emissions in mainland China," Applied Energy, Elsevier, vol. 283(C).
    5. Wang, Shaojian & Shi, Chenyi & Fang, Chuanglin & Feng, Kuishuang, 2019. "Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model," Applied Energy, Elsevier, vol. 235(C), pages 95-105.
    6. Shi, Kaifang & Chen, Yun & Li, Linyi & Huang, Chang, 2018. "Spatiotemporal variations of urban CO2 emissions in China: A multiscale perspective," Applied Energy, Elsevier, vol. 211(C), pages 218-229.
    7. Rong Wu & Yongli Zhang & Meilin Dai & Qingyin Li & Changlong Sun, 2023. "The Heterogeneity of the Drivers of Urban Form in China: Perspectives from Regional Disparities and Development Stage Variations," Land, MDPI, vol. 12(7), pages 1-20, July.
    8. Liu, Qianqian & Wang, Shaojian & Zhang, Wenzhong & Li, Jiaming & Kong, Yunlong, 2019. "Examining the effects of income inequality on CO2 emissions: Evidence from non-spatial and spatial perspectives," Applied Energy, Elsevier, vol. 236(C), pages 163-171.
    9. Lv, Zhuoran & Guo, Huadong & Zhang, Lu & Liang, Dong & Zhu, Qi & Liu, Xuting & Zhou, Heng & Liu, Yiming & Gou, Yiting & Dou, Xinyu & Chen, Guoqiang, 2024. "Urban public lighting classification method and analysis of energy and environmental effects based on SDGSAT-1 glimmer imager data," Applied Energy, Elsevier, vol. 355(C).
    10. Yuxin Liu & Chenjing Fan & Dongdong Xue, 2024. "A Review of the Effects of Urban and Green Space Forms on the Carbon Budget Using a Landscape Sustainability Framework," Sustainability, MDPI, vol. 16(5), pages 1-29, February.
    11. Xiao, Huijuan & Duan, Zhiyuan & Zhou, Ya & Zhang, Ning & Shan, Yuli & Lin, Xiyan & Liu, Guosheng, 2019. "CO2 emission patterns in shrinking and growing cities: A case study of Northeast China and the Yangtze River Delta," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    12. Cai, Bofeng & Lu, Jun & Wang, Jinnan & Dong, Huijuan & Liu, Xiaoman & Chen, Yang & Chen, Zhanming & Cong, Jianhui & Cui, Zhipeng & Dai, Chunyan & Fang, Kai & Feng, Tong & Guo, Jie & Li, Fen & Meng, Fa, 2019. "A benchmark city-level carbon dioxide emission inventory for China in 2005," Applied Energy, Elsevier, vol. 233, pages 659-673.
    13. Wang, Shaojian & Wang, Jieyu & Fang, Chuanglin & Feng, Kuishuang, 2019. "Inequalities in carbon intensity in China: A multi-scalar and multi-mechanism analysis," Applied Energy, Elsevier, vol. 254(C).
    14. Jie Su & Bo Zhou & Yuanpei Liao & Chaoshen Wang & Tian Feng, 2022. "Impact Mechanism of the Urban Network on Carbon Emissions in Rapidly Developing Regions: Example of 47 Cities in Southwest China," Land, MDPI, vol. 11(4), pages 1-19, March.
    15. Wang, Shaojian & Wang, Jieyu & Zhou, Yuquan, 2018. "Estimating the effects of socioeconomic structure on CO2 emissions in China using an econometric analysis framework," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 18-27.
    16. Hu, Ting & Huang, Xin, 2019. "A novel locally adaptive method for modeling the spatiotemporal dynamics of global electric power consumption based on DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 240(C), pages 778-792.
    17. Shi, Kaifang & Yu, Bailang & Zhou, Yuyu & Chen, Yun & Yang, Chengshu & Chen, Zuoqi & Wu, Jianping, 2019. "Spatiotemporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels," Applied Energy, Elsevier, vol. 233, pages 170-181.
    18. Wang, Shaojian & Zeng, Jingyuan & Liu, Xiaoping, 2019. "Examining the multiple impacts of technological progress on CO2 emissions in China: A panel quantile regression approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 140-150.
    19. Yang, Di & Luan, Weixin & Qiao, Lu & Pratama, Mahardhika, 2020. "Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery," Applied Energy, Elsevier, vol. 268(C).
    20. Cai, Bofeng & Cui, Can & Zhang, Da & Cao, Libin & Wu, Pengcheng & Pang, Lingyun & Zhang, Jihong & Dai, Chunyan, 2019. "China city-level greenhouse gas emissions inventory in 2015 and uncertainty analysis," Applied Energy, Elsevier, vol. 253(C), pages 1-1.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:11:y:2022:i:5:p:713-:d:811961. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.